Picture database systems are used to store and manage large amounts of picture data. Picture data can include data such as still pictures (picture data) and motion pictures (video data). Such large picture databases are difficult to browse using text search if the user wants to search for pictures having similar color content. In such large databases, picture features are added to each picture to facilitate faster retrieval of pictures of interest. Users can query such databases based on these added features to retrieve an pictures of interest. One such feature is dominant colors in a picture.
Dominant colors of a picture are defined as the colors that are perceptually dominant in the picture. For example, dominant colors of a picture of a rose plant can be the colors of the rose and the plant. Dominant colors are one of the key features the human mind generally remembers about a picture. In a large picture database, a particular picture of interest can be retrieved efficiently, by specifying one or more dominant colors. Therefore, efficient algorithms are crucial for automatic extraction of dominant colors in stored pictures.
In the past, dominant colors were determined manually by evaluation, one picture at a time, by a trained person. A manual process has two drawbacks, first the number of pictures in modern picture databases can be extremely large and the work involved in manual evaluation can be very tedious. Second in the manual process, the subjective judgment of the evaluator plays a major role in deciding the dominant colors. To overcome theses problems, automatic methods including algorithms have evolved, such as, color group methods and iterative methods for finding dominant colors in a picture stored in large databases.
Color group methods use a set of representative colors. Representative colors are decided manually or derived by quantizing the color space. The colors of each of the pixels in a picture are mapped to the representative colors. The representative colors having the most mappings are considered to be the dominant colors of the picture. However, these methods suffer from quantization effects. For example, pixels in a boundary of a dominant color can be counted in two different representative colors and not as a dominant color.
Iterative methods are automatic solutions to the problem. Here the mathematical models are designed to find representative vectors in the three-dimensional color space. In these methods, the color values of pixels are clustered to get a representative color value for each cluster. These representative colors are used as dominant colors. These methods are iterative and can result in an unknown number of clusters. Clusters that are too close to each other can end up representing visually the same colors. Clusters that are too far from each other can end up encompassing visually different colors into one color.
In a digital video sequence, flow fields of still pictures (pictures) are calculated to represent motion, edge, texture and so on. The flow fields are in two-dimensional vector space having magnitude and direction. Finding the representative vectors in these vector flow fields is a basic problem in analyzing or representing them. The mathematical formulation of this problem is also the problem of finding the representative vectors in 2D vector space. In this method, the vectors are clustered to get representative vectors. Again, this method is very iterative and can result in an unknown number of clusters. Clusters that are too close to each other represent visually the same flow vectors; and clusters that are too far from each other can end up encompassing visually different flow vectors into one representative flow vector.
Due to the above-described problems and restrictions, current algorithms to extract dominant colors from picture in large databases are generally very complex and computationally very intensive. Further, current methods measure the distance between color vectors using Euclidean in CIE-LUV color space. This requires a conversion of RGB to CIE-LUV color space. The conversion of RGB to CIE-LUV color space is very iterative, computationally intensive, and complex. Therefore, such methods generally require a lot of memory and computational resources.
Therefore, there is a need for an algorithm that can automatically extract dominant colors from pictures stored in large databases without requiring a lot of memory and computational resources. There is also a need for an algorithm that is less complex and does not require conversion of RGB to CIE-LUV color space when extracting dominant colors in stored pictures.